This package provides a graphical user interface for the yuima package.

Compute the standard expected years of life lost (YLL), as developed by the Global Burden of Disease Study (Murray, C.J., Lopez, A.D. and World Health Organization, 1996). The YLL is based on comparing the age of death to an external standard life expectancy curve. It also computes the average YLL, which highlights premature causes of death and brings attention to preventable deaths (Aragon et al., 2008).

Fits yield curves using Nelson-Siegel (1987) <doi:10.1086/296409>, Svensson (1994) <doi:10.3386/w4871>, and cubic spline methods. Extracts forward rates, discount factors, and par rates from fitted curves. Computes duration and convexity risk measures. Computes Z-spread and key rate durations. Provides principal component decomposition following Litterman and Scheinkman (1991) <doi:10.3905/jfi.1991.692347>, carry and roll-down analysis, and slope measures. All methods are pure computation with no external dependencies beyond base R; works with yield data from any source.

Analyzing performances of cricketers and cricket teams based on yaml match data from Cricsheet <https://cricsheet.org/>.

This collection of data exploration tools was developed at Yale University for the graphical exploration of complex multivariate data; barcode and gpairs now have their own packages. The big.read.table() function provided here may be useful for large files when only a subset is needed (but please see the note in the help page for this function).

This package performs nearest neighbor-based imputation using one or more alternative approaches to processing multivariate data. These include methods based on canonical correlation: analysis, canonical correspondence analysis, and a multivariate adaptation of the random forest classification and regression techniques of Leo Breiman and Adele Cutler. Additional methods are also offered. The package includes functions for comparing the results from running alternative techniques, detecting imputation targets that are notably distant from reference observations, detecting and correcting for bias, bootstrapping and building ensemble imputations, and mapping results.

This package provides a number of functions to facilitate extracting information in YAML fragments from one or multiple files, optionally structuring the information in a data.tree'. YAML (recursive acronym for "YAML ain't Markup Language") is a convention for specifying structured data in a format that is both machine- and human-readable. YAML therefore lends itself well for embedding (meta)data in plain text files, such as Markdown files. This principle is implemented in yum with minimal dependencies (i.e. only the yaml packages, and the data.tree package can be used to enable additional functionality).

This package provides functions to manage taxonomy when lineages are described with strings and ranks separated with special patterns like "|*__" or ";*__".

Fits archetypal analysis models, including Euclidean, probabilistic, kernel, and directional variants. Methods include classical archetypal analysis from Cutler and Breiman (1994) <doi:10.1080/00401706.1994.10485840>, PCHA and kernel variants from Mørup and Hansen (2012) <doi:10.1016/j.neucom.2011.06.033>, probabilistic archetypal analysis from Seth and Eugster (2016) <doi:10.1007/s10994-015-5498-8>, directional archetypal analysis from Olsen et al. (2022) <doi:10.3389/fnins.2022.911034>, AA++ initialization from Mair and Sjölund (2023) <doi:10.48550/arXiv.2301.13748>, coreset-style initialization from Mair and Brefeld (2019) <https://proceedings.neurips.cc/paper_files/paper/2019/file/7f278ad602c7f47aa76d1bfc90f20263-Paper.pdf>, and adapted AIC from Suleman (2017) <doi:10.1109/FUZZ-IEEE.2017.8015385>. Provides initialization helpers, model selection paths, plotting methods, broom methods, and a tidymodels recipe step.

Semiparametric modeling of lifetime data with crossing survival curves via Yang and Prentice model with baseline hazard/odds modeled with Bernstein polynomials. Details about the model can be found in Demarqui et al. (2019) <arXiv:1910.04475>. Model fitting can be carried out via both maximum likelihood and Bayesian approaches. The package also provides point and interval estimation for the crossing survival times.

This package provides a collection of lightweight helper functions (imps) both for interactive use and for inclusion within other packages. These include functions for minimal input assertions, visualising colour palettes, quoting user input, searching rows of a data frame and capturing string tokens.

For any spending function specified by the user, this package provides corresponding boundaries for interim testing using the adaptively weighted log-rank test developed by Yang and Prentice (2010 <doi:10.1111/j.1541-0420.2009.01243.x>). The package uses a re-sampling method to obtain stopping boundaries at the interim looks.The output consists of stopping boundaries and observed values of the test statistics at the interim looks, along with nominal p-values defined as the probability of the test exceeding the specific observed test statistic value or critical value, regardless of the test behavior at other looks. The asymptotic validity of the stopping boundaries is established in Yang (2018 <doi:10.1002/sim.7958>).

Setting layout through YAML headers in R-Markdown documents, enabling their automatic generation. Functions and methods may summarize R objects in automatic reports, for instance check-lists and further reports applied to the packages taxlist and vegtable'.

An alternative canonical correlation/redundancy analysis function, with associated print, plot, and summary methods. A method for generating helio plots is also included.

Asks Yes-No questions with variable or custom responses.

Analyze data from behavioral experiments conducted using MED-PC software developed by Med Associates Inc. Includes functions to fit exponential and hyperbolic models for delay discounting tasks, exponential mixtures for inter-response times, and Gaussian plus ramp models for peak procedure data, among others. For more details, refer to Alcala et al. (2023) <doi:10.31234/osf.io/8aq2j>.

Obtain historical and near real time data related to stocks, index and currencies from the Yahoo Finance API. This package is community maintained and is not officially supported by Yahoo'. The accuracy of data is only as correct as provided on <https://finance.yahoo.com/>.

This package provides a fast JSON parser, generator and validator which converts JSON', NDJSON (Newline Delimited JSON') and GeoJSON (Geographic JSON') data to/from R objects. The standard R data types are supported (e.g. logical, numeric, integer) with configurable handling of NULL and NA values. Data frames, atomic vectors and lists are all supported as data containers translated to/from JSON'. GeoJSON data is read in as simple features objects. This implementation wraps the yyjson C library which is available from <https://github.com/ibireme/yyjson>.

This package provides with parametric Risk Neutral Densities (RNDs) and cumulative densities of futures prices on fixed-income products. It relies on options on Short Term Interest Rate futures or options on government bond futures. It models the futures price as a mixture of lognormal densities. Leveraging on this, the package provides with the RNDs and cumulative densities of the money market rate or the government bond yield inferred from the futures price, using the RND of the futures price. The package also extracts from the RND of the government bond futures price simultaneously the RND of the cheapest-to-deliver bond yield at options maturity and the RND of the ctd bond repo rate from options maturity to futures maturity. The package also provides with the probability attached to each bond in the delivery basket of a government bond futures to be the cheapest at maturity, and also the non parametric distribution of the spread between two bond yields, using two RNDs based on options on bond futures of the same maturity. The package leverages on the works of Melick, W. R. and Thomas, C. P. (1997) <doi:10.2307/2331318> and B. Bahra (1998) <doi:10.2139/ssrn.77429>.

Dataset from the young elite swimmers study.

Semiparametric modeling of lifetime data with crossing survival curves via Yang and Prentice model with piecewise exponential baseline distribution. Details about the model can be found in Demarqui and Mayrink (2019) <arXiv:1910.02406>. Model fitting carried out via likelihood-based and Bayesian approaches. The package also provides point and interval estimation for the crossing survival times.

Download financial market data, company information, financial statements, options data, and more from the unofficial Yahoo Finance API.

Nonparametric estimation of discount functions and yield curves from transaction data of coupon paying bonds. Koo, B., La Vecchia, D., & Linton, O. B. (2021) <doi:10.1016/j.jeconom.2020.04.014> describe an application of this package using the Center for Research in Security Prices (CRSP) Bond Data and document its implementation.

The purpose of this package is to provide methods to interpret multiple linear regression and canonical correlation results including beta weights,structure coefficients, validity coefficients, product measures, relative weights, all-possible-subsets regression, dominance analysis, commonality analysis, and adjusted effect sizes.